


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A				:       str      =  logging.get_logger(__name__)

A				:       Tuple      =  {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class      UpperCamelCase(	UpperCAmelCase__ ):
					snake_case_		:					List[str]    					=						"""realm"""


					def __init__(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=3_0_5_2_2  ,     SCREAMING_SNAKE_CASE						:		Any=7_6_8  ,     SCREAMING_SNAKE_CASE						:		List[Any]=1_2_8  ,     SCREAMING_SNAKE_CASE						:		int=1_2  ,     SCREAMING_SNAKE_CASE						:		str=1_2  ,     SCREAMING_SNAKE_CASE						:		Tuple=8  ,     SCREAMING_SNAKE_CASE						:		Any=3_0_7_2  ,     SCREAMING_SNAKE_CASE						:		str="gelu_new"  ,     SCREAMING_SNAKE_CASE						:		Tuple=0.1  ,     SCREAMING_SNAKE_CASE						:		Any=0.1  ,     SCREAMING_SNAKE_CASE						:		List[str]=5_1_2  ,     SCREAMING_SNAKE_CASE						:		Tuple=2  ,     SCREAMING_SNAKE_CASE						:		List[Any]=0.02  ,     SCREAMING_SNAKE_CASE						:		int=1e-1_2  ,     SCREAMING_SNAKE_CASE						:		int=2_5_6  ,     SCREAMING_SNAKE_CASE						:		str=1_0  ,     SCREAMING_SNAKE_CASE						:		List[str]=1e-3  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=5  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=3_2_0  ,     SCREAMING_SNAKE_CASE						:		int=1_3_3_5_3_7_1_8  ,     SCREAMING_SNAKE_CASE						:		List[Any]=5_0_0_0  ,     SCREAMING_SNAKE_CASE						:		Tuple=1  ,     SCREAMING_SNAKE_CASE						:		Tuple=0  ,     SCREAMING_SNAKE_CASE						:		List[Any]=2  ,     **SCREAMING_SNAKE_CASE						:		List[str]  ,     )      ->						Dict:




											'''simple docstring'''

											super().__init__(pad_token_id=_a  ,     bos_token_id=_a  ,     eos_token_id=_a  ,     **_a    )

											# Common config
											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		retriever_proj_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		num_candidates
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps

											# Reader config
											__snake_case				=		span_hidden_size
											__snake_case				=		max_span_width
											__snake_case				=		reader_layer_norm_eps
											__snake_case				=		reader_beam_size
											__snake_case				=		reader_seq_len

											# Retrieval config
											__snake_case				=		num_block_records
											__snake_case				=		searcher_beam_size











"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



